PRODUCTIVITY IMPROVEMENT SUPPORT SYSTEM AND PRODUCTIVITY IMPROVEMENT SUPPORT METHOD

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority from Japanese application JP2020-082437, filed on May 8, 2020, the contents of which is hereby incorporated by reference into this application.

TECHNICAL FIELD

The present invention relates to a productivity improvement support system and a productivity improvement support method.

BACKGROUND ART

In the related art, there are various systems for analyzing a production system using a machine tool. For example, PTL 1 discloses a technique in which a log acquisition unit of a server device acquires an access log of a user, and a granularity setting unit sets each granularity, which is a unit for subdividing data when performing a predetermined data processing for each user, from a behavior pattern based on the access log acquired by the log acquisition unit and accumulated in a server storage unit.

CITATION LIST
Patent Literature

PTL 1: JP-A-2015-103019

SUMMARY OF INVENTION
Technical Problem

In productivity analysis using manufacturing data, a production status is estimated and a production loss factor is extracted from the combination of data at a certain time by matching a time series of each data contained in 4M data, which consists of, for example, Machine, Man, Material, and Method and shows indicators for controlling manufacturing quality. In order to determine the loss factor from the data obtained from robots and sensors, it is not possible to pick up data fluctuations (changes in a state) with a minute order, and therefore, it is necessary to analyze data fluctuations with a fine time granularity such as an ms order. However, when data is always acquired and analyzed with a fine time granularity, an amount of data and an amount of calculation become enormous, and it is practically impossible to calculate. Further, when it is no longer necessary to determine the loss factor, it is desirable to return to the original order and suppress an increase in the amount of data and the amount of calculation.

PTL 1 describes setting the granularity for each data, but does not describe combining and analyzing data having different granularity and instructing improvement of work based on an analysis result of the data having different granularity.

An object of one aspect of the invention is to provide a productivity improvement support system and a productivity improvement support method capable of instructing improvement of work based on an analysis result obtained by combining data having different granularity.

Solution to Problem

A productivity improvement support system according to one aspect of the invention includes: a time granularity setting unit configured to, when 4M data having different time granularity acquired from a target device contains data that satisfy a condition for detecting a state fluctuation, switch time granularity of the 4M data to time granularity according to a state fluctuation; a loss analysis calculation unit configured to analyze a production loss factor by using analysis model data in which the production loss factor of the target device when the condition is satisfied is determined; and a recommended work selection unit configured to select a recommended work when the production loss factor occurs from one or a plurality of recommended works by using recommended work data stored in association with the production loss factor.

Advantageous Effect

According to one aspect of the invention, it is possible to instruct improvement of work based on the analysis result obtained by combining data having different granularity.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram showing an example of a productivity improvement support system in an embodiment.

FIG. 2 is a schematic diagram of a server used in the present system.

FIG. 3 is a diagram showing examples of data stored in a 4M data storage unit, a time granularity data storage unit, an analysis model data storage unit, a loss analysis result data storage unit, and a recommended work data storage unit.

FIG. 4 is a diagram showing an example of time granularity data stored in the time granularity data storage unit.

FIG. 5 is a diagram showing an example of analysis model data stored in the analysis model data storage unit.

FIG. 6 is a diagram showing an example of loss analysis result data stored in the loss analysis result data storage unit.

FIG. 7 is a diagram showing an example of recommended work data stored in the recommended work data storage unit.

FIG. 8 is a flowchart showing a processing procedure of a processing (recommended work presentation processing) performed in the present system.

FIG. 9 is a diagram showing an example of concept of setting and switching of a time granularity in S802 and S803.

FIG. 10 is a diagram showing an example of a screen (analysis result and recommended work display screen) displayed by a display control unit on an input and output unit.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment of the invention will be described with reference to the drawings. The following description and drawings are examples for describing the invention, and are omitted and simplified as appropriate for clarification of the description. The invention can be implemented in various other forms. Unless otherwise limited, each constituent element may be singular or plural.

In order to facilitate understanding of the invention, a position, a size, a shape, a range, or the like of each constituent element shown in the drawings may not represent an actual position, size, shape, range, or the like. Therefore, the invention is not necessarily limited to the position, size, shape, range, and the like disclosed in the drawings.

In the following description, although various types of information may be described by expressions such as “table” and “list”, the various types of information may be expressed by other data structures. “XX table”, “XX list”, and the like are referred to as “XX information” to indicate that information does not depend on a data structure. When identification information is described using expressions such as “identification information”, “identifier”, “name”, “ID”, and “number”, the expressions may be replaced with each other.

When there are a plurality of constituent elements having a same or similar function, different subscripts may be attached to the same reference sign. However, when there is no need to distinguish the plurality of constituent elements, the subscripts may be omitted.

In the following description, a processing performed by executing a program may be described. However, the program is executed by a processor (for example, a central processing unit (CPU) or a graphics processing unit (GPU)) performing a predetermined processing using a storage resource (for example, a memory) and/or an interface device (for example, a communication port), or the like as appropriate, and therefore, a subject of the processing may be the processor. Similarly, the subject of the processing performed by executing the program may be a controller, device, system, computer, or node including a processor. The subject of the processing performed by executing the program may be a calculation unit, and may include a dedicated circuit (for example, a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC)) that performs a specific processing.

The program may be installed from a program source into a device such as a computer. The program source may be, for example, a program distribution server or a computer-readable storage medium. When the program source is the program distribution server, the program distribution server may include a processor and a storage resource that stores a program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to another computer. Two or more programs may be implemented as one program, or one program may be implemented as two or more programs in the following description.

The following exemplifies a case where the productivity improvement support system and the productivity improvement support method according to the present embodiment are applied at a manufacturing site such as a factory or a manufacturing line, and the productivity improvement support system and the productivity improvement support method also can be applied to various production resources related to manufacturing such as equipment, systems, devices, and instrument used at sites other than these manufacturing sites.

FIG. 1 shows an example of a productivity improvement support system 10 in the present embodiment. As shown in FIG. 1, the productivity improvement support system 10 includes an input and output unit 11, such as a keyboard, a display or a touch panel, that receives input of various information from a user, and displays a result of the various information processed by the system, a display control unit 12 that displays the result processed by the control unit 13 on a screen of the display, a control unit 13 that is a main body that executes various processes in the present system, and a storage unit 14 that stores various data used in the present system.

In addition, the display control unit 12 includes a loss analysis result display control unit 121 that displays a loss analysis result calculated by a loss analysis calculation unit 133, which will be described later, on the display screen, and a recommended work display control unit 122 that displays a recommended work calculated by a recommended work selection unit 134, which will be described later, on the display screen.

Further, the control unit 13 is configured by a server including a data extraction unit 131 that acquires 4M data from a target device that outputs 4M data, such as a machine tool 21, a camera 22, a robot 23, and a sensor 24 installed in a factory or a manufacturing line, and stores manufacturing record data to be described later in a 4M data storage unit 141, a time granularity setting unit 132 that sets or switches a time granularity of the 4M data extracted by the data extraction unit 131 when it is determined that a fluctuation of the acquired 4M data, that is, the state fluctuation in a process of producing the target device has occurred more than a certain level by using a time granularity data 1421 to be described later, a loss analysis calculation unit 133 that analyzes a production loss factor of the target device that outputs the 4M data, in which the time granularity is switched, by using an analysis model data 1431 to be described later, and a recommended work selection unit 134 that selects a work recommended according to the analysis result of the production loss.

Moreover, the storage unit 14 includes a 4M data storage unit 141 that stores the 4M data acquired and extracted from the target device, a time granularity data storage unit 142 that stores the time granularity set or switched by the time granularity setting unit 131, an analysis model data storage unit 143 that defines a condition for detecting the production loss factor of the target device, a loss analysis result data storage unit 144 that stores the loss factor detected by the analysis model data 1431 described above, and a recommended work data storage unit 145 that defines a recommended work corresponding to the loss factor.

As shown in FIG. 2 (a schematic diagram of a computer), the server shown above can be realized by a general computer 200 including a CPU 201, a memory 202, an external storage device 203 such as a hard disk drive (HDD), a read and write device 207 that reads and writes information of a portable storage medium 208 such as a compact disk (CD) or a digital versatile disk (DVD), an input device 206 such as a keyboard or a mouse, an output device 205 such as a display, a communication device 204 such as a network interface card (NIC) for connecting to a communication network, and internal communication lines 209 (referred to as system buses) such as system buses that connect these.

For example, each DB of the 4M data storage unit 141, the time granularity data storage unit 142, the analysis model data storage unit 143, the loss analysis result data storage unit 144, the recommended work data storage unit 145 and the like stored in the server can be realized by the CPU 201 reading from the memory 202 or the external storage device 203 and using it. In addition, the data extraction unit 131, the time granularity setting unit 132, the loss analysis calculation unit 133, and the recommended work selection unit 134 included in the server can be realized by the CPU 201 loading a predetermined program stored in the external storage device 203 into the memory 202 and executing the program. Further, the server may have the input and output unit 11 in which the CPU 201 can operate the input device 206 to realize an input function. Further, the server may have the input and output unit 11 in which the CPU 201 can operate the output device 205 to realize an output function. Further, the server may have a communication unit (not shown) in which the CPU 201 can operate the communication device 204 to realize a communication function. In the present embodiment, the data extraction unit 131 of the server has a function controlled by the communication unit described above. The data extraction unit 131 acquires the 4M data described above from the machine tool 21, the camera 22, the robot 23, and the sensors 24 via a network N.

FIG. 3 is a diagram showing an example of the data stored in the 4M data storage unit 141, the time granularity data storage unit 142, the analysis model data storage unit 143, the loss analysis result data storage unit 144, and the recommended work data storage unit 145 in the storage unit 14. FIG. 3 illustrates Machine data 1411 as an example of the data stored in the 4M data storage unit 141. FIG. 3 further illustrates Man data 1412 as an example of the data stored in the 4M data storage unit 141. FIG. 3 further illustrates Material data 1413 as an example of the data stored in the 4M data storage unit 141. FIG. 3 further illustrates Method data 1414 as an example of the data stored in the 4M data storage unit 141.

As shown in FIG. 3, the Machine data 1411 includes an equipment ID for identifying a target device that outputs the 4M data, a start time when the target device such as a machine tool identified by the equipment ID operates, an end time of the operation of the target device, an operating status of the target device, an execution recipe that shows a condition for the target device to operate, a past cumulative operating time of the target device, and a current value during the operation of the target device.

In addition, the Man data 1412 includes a worker ID, a start time when a worker such as a worker identified by the worker ID starts the operation, an end time when the worker finishes the operation, a working area and a working time of the worker, and a CNC operation indicating whether there is an operation on a controller of the target device.

Further, the Material data 1413 includes a product ID of a product manufactured by the equipment, an ID reading indicating a reading state of the product ID, an ID reading time, which is a time when the ID reading is performed, a process transport indicating a transport state of the product, a start time and an end time of the transport, and a quality and a temperature of the product.

Further, the Method data 1414 includes a Method ID for identifying the process, a recipe ID indicating a procedure of the process identified by the Method ID, a work instruction No indicating an order of the process, and a work start time and work end time of the process.

FIG. 4 is a diagram showing an example of time granularity data 1421 stored in the time granularity data storage unit 142. The time granularity data 1421 is data in which the time granularity of the 4M data acquired from the target device is stored. As shown in FIG. 4, in the time granularity data 1421, a group ID for identifying a group of time granularity that is set or switched by the time granularity setting unit 131, a start time when a target device such as a machine tool operates, an end time when the operation of the target device ends, a data group for identifying the data for each type of the 4M data, data content indicating the content of the data identified by the data group, and the time granularity of the data identified by the data group are stored in association with each other. In FIG. 4, for example, a “current value” of a data group “M0003” belonging to a group ID “G0001” indicates that a time granularity on an order of “ms” is set. This further indicates that a time when the current value detection starts is “13:01” on “Nov. 1, 2019”, and a time when the current value detection ends is “13:15” on “Nov. 1, 2019”. The time granularity set in the time granularity data 1421 is set by the time granularity setting unit 132, and as will be described later, the time granularity is switched when it is determined that the state fluctuation has occurred more than a certain level in a process of producing the target device.

FIG. 5 is a diagram showing an example of the analysis model data 1431 stored in the analysis model data storage unit 143. The analysis model data 1431 is data that defines conditions for detecting the state fluctuation in the process of producing the target device. As shown in FIG. 5, in the analysis model data 1431, a loss ID for identifying a production loss factor, a production loss factor identified by the loss ID, and the conditions for detecting the state fluctuation in the process of producing the target device that outputs 4M data are stored in association with each other. In FIG. 5, regarding the Machine among the 4M data, the conditions are set separately as robots and others. For example, the production loss factor identified by a loss ID “L0002” is “robot stop due to arm overload”. The condition for detecting the production loss factor is that the Machine is in a stopped state, when the Material in Method “W0002” is “good” and it is detected that the current value of Robot exceeds 0.5, it is determined that the production loss factor is the “robot stop due to arm overload” described above. In addition, the state fluctuation is detected only when the production loss state is switched. For example, in FIG. 5, when the current value fluctuates from 0.1 to 0.3, the current value itself fluctuates, but the fluctuation in the production loss state is not detected.

FIG. 6 is a diagram showing an example of the loss analysis result data 1441 stored in the loss analysis result data storage unit 144. As shown in FIG. 6, in the loss analysis result data 1441, an equipment ID for identifying a target device that outputs the 4M data, a start time when a target device such as a machine tool identified by the equipment ID operates, an end time when the operation of the target device ends, a loss ID for identifying a production loss factor, and a production loss factor identified by the loss ID are stored in association with each other. FIG. 6 shows that, for example, in Machine (for example, Robot) identified by an equipment ID “M0002”, a time when the current value detection starts is “13:01” on “Nov. 1, 2019”, a time when the current value detection ends is “13:15” on “Nov. 1, 2019”, and the production loss factor in which the state fluctuation has occurred in the detection is “robot stop due to arm overload” identified by the loss ID “L0002”.

FIG. 7 is a diagram showing an example of recommended work data 1451 stored in the recommended work data storage unit 145. As shown in FIG. 7, in the recommended work data 1451, a loss ID for identifying a production loss factor, a production loss factor identified by the loss ID, and a recommended work that indicates a recommended work for the production loss factor are stored in association with each other. FIG. 7 shows that for example, when the production loss factor of “robot stop due to arm overload” identified by the loss ID “L0002” occurs, the recommended work is “arm position accuracy adjustment”.

FIG. 8 is a flowchart showing a processing procedure of processing (recommended work presentation processing) performed in the present system. As shown in FIG. 8, first, the data extraction unit 131 acquires the 4M data from the target device that outputs the 4M data, acquires the manufacturing record data as shown in FIG. 3, and stores the data in the 4M data storage unit 141 (S801).

Subsequently, the time granularity setting unit 132 determines whether it is detected that the fluctuation of the acquired 4M data has occurred more than a certain level with reference to the time granularity data 1421 shown in FIG. 4. Then, the time granularity setting unit 132 sets or switches the time granularity of the 4M data acquired by the data extraction unit 131 when it is determined that the state fluctuation in the process of producing the target device has occurred more than a certain level (S802, S803).

Here, the concept of setting and switching the time granularity in S802 and S803 will be described. FIG. 9 is a diagram showing an example of the concept of setting and switching the time granularity in S802 and S803. FIG. 9 illustrates the Man data 1412 and the Machine data 1411 (in FIG. 9, two data of Machine data and Robot data), among the 4M data. As shown in FIG. 9, from a certain time t0 to a time t1, in the Man data 1412 and the Machine data 1411 acquired by the data extraction unit 131, the state fluctuation in the process of producing the target device is not detected. Therefore, the data extraction unit 131 acquires the 4M data from the target device in minutes, which is the time granularity (for example, the default time granularity) when the above state fluctuation is not detected, with reference to the time granularity data 1421.

Then, the time granularity setting unit 132 determines whether the data extraction unit 131 has acquired 4M data (for example, current value >0.5) that satisfies the conditions for detecting the state fluctuation with reference to the analysis model data 1431. The time granularity setting unit 132 switches the time granularity of the 4M data extracted by the data extraction unit 131 to a smaller time granularity than before, and updates the value to the time granularity of the time granularity data 1421, when it is determined that the data extraction unit 131 has acquired the 4M data that satisfies the conditions for detecting the state fluctuation. In FIG. 9, since the state fluctuation describe above is detected at a certain time t1, the time granularity setting unit 132 updates the time granularity in a minute, which is a time granularity when the state fluctuation is detected (for example, time granularity less than the default time granularity), from the time t1 to a time t2. Then, the data extraction unit 131 starts to acquire 4M data from the target device with the time granularity when the state fluctuation is detected (100 ms in FIG. 9) with reference to the updated time granularity data 1421.

After that, from a time t2 to a time t3, the data extraction unit 131 acquires 4M data from the target device with the updated time granularity. Then, when it is determined that the data extraction unit 131 has acquired 4M data (for example, current value ≤0.5) that does not satisfy the conditions for detecting the state fluctuation in the process of producing the target device, the time granularity setting unit 132 returns the time granularity of the 4M data extracted by the data extraction unit 131 from the switched time granularity to the default time granularity, and sets the value to the time granularity of the time granularity data 1421. In FIG. 9, since the state fluctuation is not detected at a certain time t3, the time granularity setting unit 132 sets the time granularity in minutes, which is the time granularity (for example, the default time granularity) when the state fluctuation is not detected from the time t3 to a time t4. Then, the data extraction unit 131 starts to acquire 4M data from the target device with the time granularity (in minutes in FIG. 9) when the state fluctuation is not detected with reference to the set time granularity data 1421. After that, as in the case described above, when the state fluctuation is detected, the data extraction unit 131 acquires 4M data from the target device with the time granularity smaller than that in a normal time, and when the state fluctuation is not detected, returns to a time granularity in the normal time and acquires 4M data from the target device.

Returning to FIG. 8, the loss analysis calculation unit 133 analyzes the production loss factor of the target device that outputs the 4M data in which the time granularity described above is switched with reference to the analysis model data 1431 (S804). For example, the loss analysis calculation unit 133 reads the production loss factor corresponding to the 4M data that satisfies the conditions for detecting the state fluctuation in the analysis model data 1431, determines that the state fluctuation is due to the read production loss factor, and outputs the loss analysis result data 1441 as shown in FIG. 6, including the production loss factor and the loss ID. The loss analysis calculation unit 133 sets, for example, the start time and end time of the time granularity data 1421 as the start time and end time of the loss analysis result data 1441.

The recommended work selection unit 134 selects the recommended work according to the analysis result of the production loss and presents the result (S805) with reference to the recommended work data 1451 shown in FIG. 7. For example, the recommended work selection unit 134 reads the recommended work corresponding to the same loss ID and production loss factor as the loss ID and production loss factor of the loss analysis result data 1441 from the recommended work data 1451 shown in FIG. 7, and outputs the result to the display control unit 12. The display control unit 12 displays the output result described above to the input and output unit 11.

FIG. 10 is a diagram showing an example of a screen (a display screen of the analysis result and the recommended work) displayed on the input and output unit 11 by the display control unit 12. As shown in FIG. 10, the display control unit 12 outputs a screen including a loss analysis result display unit 1001, a recommended work display unit 1002, and a detailed loss factor display unit 1003 as the display screen of the analysis result and the recommended work.

The loss analysis result display unit 1001 includes the same contents as the conceptual diagram of the setting and switching of the time granularity shown in FIG. 9. The display control unit 12 displays, for example, 4M data in time series, with a vertical axis representing a name of each 4M data and a horizontal axis representing a time, and displays the set or switched time granularity on the screen, with reference to each 4M data shown in FIG. 3, the time granularity data 1421 shown in FIG. 4, and the analysis model data 1431 shown in FIG. 5.

In addition, the recommended work display unit 1002 includes a date and time and granularity column 1012 showing the date and time and the granularity when the time granularity is switched due to the fluctuation of the 4M data, a control execution history column 1013 showing the production loss factor when the fluctuation occurs, and a recommended work column 1014 showing the recommended work for the production loss which is the factor. The display control unit 12 reads the time granularity data 1421 shown in FIG. 4, and displays the start time and end time when the fluctuation of the 4M data has occurred, and the time granularity at that time in the date and time and granularity column 1012.

The display control unit 12 reads the analysis model data 1431 shown in FIG. 5 or the loss analysis result data 1441 shown in FIG. 6, and displays the production loss factor at the start time and end time when the fluctuation occurs in the control execution history column 1013. The display control unit 12 further reads the recommended work data 1451 shown in FIG. 7, and displays the recommended work corresponding to the production loss factor displayed in the control execution history column 1013 described above in the recommended work column 1014.

In addition, the detailed loss factor display unit 1003 includes all or a part of the contents of the loss analysis result data 1441 shown in FIG. 6 and the time granularity data 1421 shown in FIG. 4. Since the display screen of the analysis result and recommended work as shown in FIG. 10 is presented to the user, the user can easily grasp the production loss factor at that time even when the combination of the manufacturing record data having different time granularity is analyzed, or the state fluctuation is detected in any of 4M data. In addition, the recommended work when the production loss factor occurs can be easily confirmed.

Therefore, since the present system includes the time granularity setting unit 132 that switches the time granularity of the 4M data to the time granularity according to the state fluctuation when the 4M data having different time granularity (for example, each 4M data shown in FIG. 3) acquired from the target device outputting the 4M data contains data that satisfy the conditions for detecting the state fluctuation (for example, the conditions determined in the analysis model data 1431), the loss analysis calculation unit 133 that analyzes the production loss factor by using the analysis model data 1431 in which the production loss factor of the target device when the conditions are satisfied is determined, and the recommended work selection unit 134 that selects the recommended work when the production loss factor occurs from one or a plurality of recommended works by using the recommended work data stored in association with the production loss factor (for example, the recommended work data 1451), it is possible to instruct the improvement of work according to the production loss factor when the state fluctuation occurs based on the analysis result obtained by combining data having different granularity.

In addition, since the display control unit 12 that outputs a screen including the 4M data, the time granularity after switching, the production loss factor, and the recommended work to the display unit is provided, the user can grasp the information at a glance.

In addition, since the recommended work selection unit selects the recommended work from the recommended work data according to the time granularity, the recommended work according to the time granularity can be presented to the user.

In addition, when the time granularity setting unit does not include the data that satisfies the conditions for detecting the state fluctuation, the time granularity switched according to the state fluctuation is returned to the time granularity before detecting the state fluctuation, and as a result, it is not necessary to analyze data with fine time granularity, and it is possible to suppress an increase in the amount of data and the amount of calculation.

In the related art, when analyzing a combination of the manufacturing record data having different time granularity, the amount of data would be enormous if the analysis is performed according to the minimum time granularity among 4M data, and it is not possible to perform the calculation in reality, but according to the present system, as described above, it is possible to analyze the loss factor by setting the required time granularity for each data. For example, it is possible to set the time granularity required for analysis for a combination of manufacturing data, perform the analysis by combining data having different time granularity, and set or change improvement measures according to the time granularity at the time of the analysis. Thus, the time granularity for the data combination is switched to save and analyze the data so as to pick up the detailed data only before and after data fluctuation (change in the state) occurs, and the work instruction (display) for equipment control/work is changed according to a controllable time granularity, so that it is possible to identify the root cause of the production loss, which cannot be realized by combining techniques of the related art, and to recommend the measures necessary to improve productivity.

Although the invention has been described in detail based on the embodiment, the invention is not limited to the embodiments described above, and various modifications can be made without departing from the scope of the invention.

REFERENCE SIGN LIST

10 productivity improvement support system

11 input and output unit

12 display control unit

121 loss analysis result display control unit

122 recommended work display control unit

13 control unit

131 data extraction unit

132 time granularity setting unit

133 loss analysis calculation unit

134 recommended work selection unit

14 storage unit

141 4M data storage unit

1411 Machine data

1412 Man data

1413 Material data

1414 Method data

142 time granularity data storage unit

1421 time granularity data

143 analysis model data storage unit

1431 analysis model data

144 loss analysis result data storage unit

1441 loss analysis result data

145 recommended work data storage unit

1451 recommended work data

PRODUCTIVITY IMPROVEMENT SUPPORT SYSTEM AND PRODUCTIVITY IMPROVEMENT SUPPORT METHOD

Information

Publication Number

Date Filed

Date Published

Inventors

Original Assignees

CPC

International Classifications

Abstract

Description

Claims

Priority Claims (1)